Evaluation of qPCR reference genes for taimen (Hucho taimen) under heat stress
Bibliographic record
Abstract
As a powerful and attractive method for detecting gene expression, qRT-PCR has been broadly used in aquaculture research. Understanding the biology of taimen (Hucho taimen) has drawn increasing interest because of its ecological and economic value. Stable reference genes are required for the reliable quantification of gene expression, but such genes have not yet been optimized for taimen. In this study, the stability levels of 10 commonly used candidate reference genes were evaluated using geNorm, NormFinder, BestKeeper, and RefFinder. The expression levels of the 10 genes were detected using 240 samples from 48 experimental groups consisting of 40 individuals treated under four heat-stress conditions (18, 20, 22, and 24 °C) for 24 h and 26 °C for 4, 24, 48, and 72 h. Six tissues (blood, heart, brain, gill, skin, and liver) were collected from each individual. Ribosomal protein S29 (RPS29) and ribosomal protein L19 (RPL19) were the most stable genes among all of the samples, whereas 28S ribosomal RNA (28S rRNA), attachment region binding protein (ARBP), and 18S ribosomal RNA (18S rRNA) were the least stable. These results were verified by an expression analysis of taimen heat-stress genes (heat shock protein 60, hsp60, and heat shock protein 70, hsp70). In conclusion, RPS29 and RPL19 are the optimal reference genes for qRT-PCR analyses of taimen, irrespective of the tissue and experimental conditions. These results allow the reliable study of gene expression in taimen.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".